1.
Towards Seed-Free Music Playlist
Generation:
Enhancing Collaborative Filtering with Playlist Title
Information
Jaehun Kim, Minz Won, Cynthia C. S. Liem, Alan Hanjalic
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2.
Preliminary Approach

3.
First attempt : WRMF
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● Good Old MF
○ Weighted Regularized Matrix Factorization [1]
■ Developed for implicit feedback
■ ALS* optimization : fast and reliable
■ Only 2~3 hyper parameters
*Alternating Least Square (or Coordinate Descent)
R U~= x V

4.
First attempt : WRMF
4

5.
First attempt : WRMF
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6.
First attempt : WRMF
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NO SEED
Average
Winner

7.
First attempt : WRMF
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● Good Old MF
○ Already reasonable performance
○ Except No-Seed case => Cold Start Problem
● Any metadata or content for playlist?

8.
First attempt : WRMF
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● Good Old MF
○ Already reasonable performance
○ Except No-Seed case => Cold Start Problem
● Any metadata or content for playlist?
○ Playlist titles!

9.
Playlist Titles

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● Text information (implicitly) represents the playlist
● Some key statistics
Playlist Titles
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# of titles (MPD + Challenge Set) 1,010,000
# of unique titles 93,250
# of unique titles (stemmed) 49,808
Single word ~60%
Less than two words ~92%

11.
● Text information (implicitly) represents the playlist
● Some key statistics
Playlist Titles
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# of titles (MPD + Challenge Set) 1,010,000
# of unique titles 93,250
# of unique titles (stemmed) 49,808
Single word ~60%
Less than two words ~92%
1. Playlist titles ~= Words

12.
● Noisiness
Playlist Titles
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Categories Examples
Special characters //Pretty Little Liars//, ** some tunes, ?!?
Repeated characters Yaaaas, summerrrr, partayyy
Shortened words Chillin, Temp, favss
Abbreviated words loml, jb, IDFK, jjjj
Symbolic expressions
Multiple languages otoño, 電台收藏, アニメ

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● Noisiness
Playlist Titles
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Categories Examples
Special characters //Pretty Little Liars//, ** some tunes, ?!?
Repeated characters Yaaaas, summerrrr, partayyy
Shortened words Chillin, Temp, favss
Abbreviated words loml, jb, IDFK, jjjj
Symbolic expressions
Multiple languages otoño, 電台收藏, アニメ
2. Standard word-level approaches
(may be) NOT WORKING

14.
Playlist Titles
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● Playlist titles ~= words
● Standard word-level approaches (may be) not working
● Character level approach : Character N-GRAM

15.
Character N-gram
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Input Text “Character N-gram”
Unigram (1-gram) C, h, a, r, a, c, t, e, r, , N, -, g, r, a, m
Bigram (2-gram) Ch, ha, ar, ac, ct, te, er, r , N, N-, -g, ...
Trigram (3-gram) Cha, har, arc, rct, cte, ter, er , r N, N-, ...
Quadrogram (4-gram) Char, harc, arct, rcte, cter, ter , er N, ...
... ...

16.
Character N-gram
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Input Text “Character N-gram”
Unigram (1-gram) C, h, a, r, a, c, t, e, r, , N, -, g, r, a, m
Bigram (2-gram) Ch, ha, ar, ac, ct, te, er, r , N, N-, -g, ...
Trigram (3-gram) Cha, har, arc, rct, cte, ter, er , r N, N-, ...
Quadrogram (4-gram) Char, harc, arct, rcte, cter, ter , er N, ...
... ...
Cha har arc cte ter er r N N-abc zeb ytj pyk nrc nfe
1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 ...
jkl
Bag-of-Character-N-gram

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● Build bag-of-n-grams for each playlist (Train + Test Set)
● For each testing playlist
○ Find M closest playlist in Train set using cosine distance
○ Collect tracks from retrieved playlist
○ Recommend L most popular tracks
Title-based RecSys
NGRAM:Similarity Based
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18.
Cosine
Distance
Title-based RecSys
NGRAM:Similarity Based
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Cha har arc cte ter er r N N-abc zeb ytj pyk nrc nfe
1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 ...
jkl
...
...
Testing
Playlists
Training
Playlist
L most popular tracks among
M closest training playlists
Bag-of-Character-N-gram

19.
Title-based RecSys
:: Model Based
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20.
Title-based RecSys
:: Model Based
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1.
(pre-trained)
Transfer`Title info’

21.
Title-based RecSys
:: Model Based
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1. 2.
Substitute

22.
Title-based RecSys
:: Model Based
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1. 2.
?X. Wang et al. (2014) [2] A. Van den Oord et al. (2013) [3]

23.
Title-based RecSys
:: Model Based
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1. 2.
+
?!

24.
Proposed Model

25.
Multi-Objective Collab. Filtering
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MF for Pre-trained Factors
Main objective function for

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Multi-Objective Collab. Filtering
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1. 2.

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Multi-Objective Collab. Filtering
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1. 2.

28.
RNCF : Recurrent Neural CF
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29.
Results

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Results:Overall
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● It works! (10th on final Leaderboard)

31.
Results:Overall
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● Superior over simple baselines

32.
Results:Overall
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● WRMF > SVD

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HRNCF : Hybrid RNCF
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R U~= x V
Filling missing
factors

34.
Results:Overall
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● HRNCF: best of both worlds

35.
Results:Overall
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● Simple NGRAM distance may have
given us very similar performance

36.
Hyperparameters: WRMF
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37.
Hyperparameters: WRMF
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38.
Hyperparameters: RNCF
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39.
Hyperparameters: RNCF
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40.
Hyperparameters: RNCF
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Discussion & Take away

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Take away
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● MF is still powerful
● Setting up right (internal) evaluation setup is more important than model
● Software engineering DOES MATTER
○ Since the scalability DOES MATTER
○ Since hyper-parameter tuning DOES MATTER
● Deep learning is not a magic wand
○ No Free Lunch
○ It costs a LOT
● Content-based algorithms still gives small (but significant) gain to CF

43.
Thank you!
Code: https://github.com/eldrin/recsys18-spotify-spotif-ai
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44.
References
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[1] Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." Data Mining, 2008.
ICDM'08. Eighth IEEE International Conference on. Ieee, 2008.
[2] Wang, Xinxi, and Ye Wang. "Improving content-based and hybrid music recommendation using deep learning."
Proceedings of the 22nd ACM international conference on Multimedia. ACM, 2014.
[3] Van den Oord, Aäron, Sander Dieleman, and Benjamin Schrauwen. "Deep content-based music recommendation."
Advances in neural information processing systems. 2013.